Note that our distribution calibration strategy is over the feature-level and is agnostic to any feature extractor.
Thus, it can be built on top of any pretrained feature extractors without further costly fine-tuning.
In our experiments, we use the pretrained WideResNet [1]} following previous work ([2]}). The WideResNet is trained to classify the base classes, along with a self-supervised pretext task to learn the general-purpose representations suitable for image understanding tasks. Please refer to their paper for more details on training the feature extractor.
